Automated profiling of resource usage

ABSTRACT

Operating profiles for consumers of computing resources may be automatically determined based on an analysis of actual resource usage measurements and other operating metrics. Measurements may be taken while a consumer, such as a virtual machine instance, uses computing resources, such as those provided by a host. A profile may be dynamically determined based on those measurements. Profiles may be generalized such that groups of consumers with similar usage profiles are associated with a single profile. Assignment decisions may be made based on the profiles, and computing resources may be reallocated or oversubscribed if the profiles indicate that the consumers are unlikely to fully utilize the resources reserved for them. Oversubscribed resources may be monitored, and consumers may be transferred to different resource providers if contention for resources is too high.

BACKGROUND

Generally described, computing devices utilize a communication network, or a series of communication networks, to exchange data. Companies and organizations operate computer networks that interconnect a number of computing devices to support operations or provide services to third parties. The computing systems can be located in a single geographic location or located in multiple, distinct geographic locations (e.g., interconnected via private or public communication networks). Specifically, data centers or data processing centers, herein generally referred to as “data centers,” may include a number of interconnected computing systems to provide computing resources to users of the data center. The data centers may be private data centers operated on behalf of an organization or public data centers operated on behalf, or for the benefit of, the general public.

To facilitate increased utilization of data center resources, virtualization technologies may allow a single physical host computing device to host one or more instances of virtual machine instance configurations that appear and operate as independent computing devices to users of a data center. With virtualization, the single physical host computing device can create, maintain, delete, or otherwise manage virtual machine instances in a dynamic manner. In turn, users can request single computing devices or a configuration of networked computing devices, and be provided with varying numbers of virtual machine resources.

The computing resources provided by the host computing devices may include computing capacity, memory and other storage, bandwidth, and the like. In a data center environment with thousands of host computing devices, an instance of a virtual machine may be instantiated on a random host computing device so long as the target host computing device meets specified criteria such as sufficient and available computing device resources (e.g., processing units, memory, and the like). Once an instance of a virtual machine is instantiated on a physical host computing device, a predetermined amount of one or more computing resources may be reserved for use by the virtual machine instance. A computing resource provider or other operator of the data center environment may guarantee availability, to the virtual machine instance, of the reserved amounts of computing resources on the target computing device.

BRIEF DESCRIPTION OF DRAWINGS

Throughout the drawings, reference numbers may be re-used to indicate correspondence between referenced elements. The drawings are provided to illustrate example embodiments described herein and are not intended to limit the scope of the disclosure.

FIG. 1 is a block diagram of an illustrative network computing environment including a management component, multiple host computing devices, and multiple customers.

FIG. 2 is a block diagram of an illustrative management component including various modules and storage components.

FIG. 3 is a flow diagram of an illustrative process for automatically profiling computing resource usage.

FIG. 4 is a block diagram of an illustrative host computing device hosting virtual machines which utilize computing resources provided by the computing device.

FIG. 5 is a flow diagram of an illustrative process for launching virtual machines on host computing devices, allocating and oversubscribing computing resources, and migrating currently executing virtual machines in order to further optimize computing resource utilization.

FIG. 6 is a block diagram of an illustrative host computing device in which various computing resources are oversubscribed and excess capacity remains available.

FIG. 7 is a block diagram of an illustrative host computing device in which various computing resources are substantially oversubscribed due to minimal resource utilization of each virtual machine executing on the host computing device.

FIG. 8 is a block diagram of an illustrative migration of a virtual machine from one host computing device to another host computing device due to oversubscription and a lack of available computing resource capacity.

DETAILED DESCRIPTION

Generally described, the present disclosure relates to computing resource utilization. Specifically, the disclosure relates to automatically determining resource usage and operating metric profiles for consumers of computing resources based on an analysis of actual resource usage measurements and other operating metrics. In some embodiments, a consumer of computing resources, such as an instance of a virtual machine instantiated according to customer specifications (e.g.: a virtual machine instance instantiated from a virtual machine image configured with an operating system and application software according to customer specifications), may be instantiated on a host physical computing device. The virtual machine instance may consume various computing resources based on the execution of one or more computer software programs or other workloads by the virtual machine instance. The virtual machine instance can then terminate execution or otherwise be configured for a different purpose.

For specific entities, such as a customer or set of customers, the process of instantiating virtual machine instances may be repeated. As part of processing the lifecycle of the virtual machine instance, a service provider associated with providing the virtual machine instances can observe and record resource consumption. The service provider can then determine a virtual machine instance resource usage and operating metric profile based on processing resource consumption measurements and other operating metric information.

Additional aspects of the disclosure relate to generalizing the resource usage and operating metric profiles, generally referred to as operating profiles. For example, thousands or more of virtual machine instances may be instantiated and may utilize resources in a single network computing environment, such as a data center. Rather than determining and maintaining separate customized operating profiles for each virtual machine instance, virtual machine instances may be assigned to generalized or default operating profiles. Accordingly, groups of virtual machine instances may be categorized according to a variety of organizational criteria and assigned to the same operating profile. In some cases, the operating profiles may be hierarchical, such that a particular virtual machine instance configuration is associated with a particular operating profile, and also with a more general operating profile that is itself associated with multiple virtual machine instance configurations. There may be multiple levels to the hierarchy, with potentially thousands of virtual machine instance configurations associated with a single general operating profile or a small number of top-level operating profiles, and a larger number of more specific operating profiles at each level within the hierarchy. At the bottom level of the hierarchy may be a particular operating profile associated with a single virtual machine instance configuration as used by a particular customer.

Further aspects of the disclosure relate to identifying a target host computing device to provide computing resources to virtual machine instances based on an automatically determined operating profile. In some embodiments, particular physical host computing devices may be configured to provide target computing resources to multiple virtual machine instances concurrently. A predetermined amount of a computing resource may be reserved for use by a single virtual machine instance. When the operating profile for a virtual machine instance indicates that the virtual machine instance will not likely consume computing device resources that have been reserved for the instance, the host computing device may instantiate additional virtual machine instances. The additional virtual machine instances may be associated with, or have access to, host computing device resources concurrently with the previously instantiated virtual machine instances. Additionally, if any of the virtual machine instances consumes or otherwise restricts access to a resource such that the consumption meets or exceeds one or more thresholds specified in an operating profile for the virtual machine instance, one or more of the virtual machine instances may be transferred to another host computing device. For example, the virtual machine instance associated with consumption that exceeds a threshold may be transferred, or other virtual machine instances may be transferred.

Some computing resources are not necessarily provided by the host computing devices, but rather are off-host resources. For example, network topology may require communications from one host computing device to a second host computing device to traverse one or more links (e.g., network connections between switches and other network components). The number of links may be different for communications to a third host computing device. Data regarding link traffic and the number of links that communications will traverse between hosts may be recorded as a resource consumption measurement or operating metric. The data may be used in the future to determine on which host computing device to instantiate a virtual machine instance that may communicate with another host computing device. In addition, consumption of some computing resources does not necessarily reduce the amount of the resource that is available for other virtual machine instances or consumers generally. For example, a feature provided by a host computing device, such as a particular instruction set, may be generally referred to as a computing resource. Usage of the instruction set, however, does not necessarily reduce availability of the instruction set to another virtual machine instance, application, or other consumer.

Although aspects of the embodiments described in the disclosure will focus, for the purpose of illustration, on relationships and interactions between a management component, server computing devices, and virtual machines instantiated on the server computing devices on behalf of customers, one skilled in the art will appreciate that the techniques disclosed herein may be applied to any number of hardware or software processes or applications. For example, while virtual machine instances will be generally used as the illustrative computing resource consumer, other programs or workloads may be substituted, such as application software, operating systems, storage area network (SAN) nodes, and the like. In addition, while computing resources such as memory, CPU capacity, and network bandwidth will be used as the illustrative computing resources, other computing resources may be substituted, such as network link traffic, latency, processor instruction sets, and the like. Further, although various aspects of the disclosure will be described with regard to illustrative examples and embodiments, one skilled in the art will appreciate that the disclosed embodiments and examples should not be construed as limiting. Various aspects of the disclosure will now be described with regard to certain examples and embodiments, which are intended to illustrate but not limit the disclosure.

FIG. 1 illustrates an example network computing environment 100 in which automated profiling of resource usage and assignment of resources based on those profiles may be implemented. Operating profiles and assignment of resources can be based on prior measurements of actual resource usage and other operating metrics, and also on expected future usage of resources. A network computing environment 100 can include a management component 102 and any number of physical host computing devices 104 a-104 n in communication via a network 110. One or more customers 122 may communicate with the components of the network computing environment 100 via a network 120.

Network computing environments 100 such as the one illustrated in FIG. 1 may be implemented in data centers and other environments in which multiple host computing devices 104 a-104 n provide computing services and resources to internal or external customers 122. As described in more detail below, each customer 122 may connect to the management component 102 or some other component within the network computing environment 100 to initiate computing processes. The initiation of computing processes may include instantiation of a virtual machine instance on a host computing device 104 or the configuration of an operating environment and one or more software applications. The virtual machine instance may execute on behalf of the user, consuming computing resources of the host computing device 104, network 110, and the like. While the present disclosure will focus, for purposes of illustration only, on the operation of a network computing environment 100 providing computing services to external or internal customers 122 through the use of virtual machines, the systems and processes described herein may apply to any implementation of a network computing environment 100, including one with no separate customer 122 entities or no virtual machine usage.

Each host computing device 104 may be a server computer, such as a blade server. Optionally, a host computing device 104 may be a midrange computing device, a mainframe computer, a desktop computer, or any other computing device configured to provide computing services and resources to multiple consumers, such as virtual machine instances, concurrently. In a typical implementation, a host computing device 104 can be configured to communicate with other host computing devices 104, a management component 102, or some other component of the network computing environment 100 via a network 110.

The network 110 may be a local area network (LAN), wide area network (WAN), some other network, or a combination thereof. In addition, the network computing environment 100 may connect to another network 120, such as a corporate or university network, or a collection of networks operated by independent entities, such as the Internet. Customers 122 of the network computing environment 100 may communicate with host computing devices 104 over the combination of the networks 120, 110. In some embodiments, the customers 122 may cause a computing device 102 to launch a virtual machine instance to execute various computing operations for or on behalf of the customer 122. Any number of virtual machine instances may be running on a single host computing device 104 at a given time. In addition, the various virtual machine instances running on a host computing device 104 may be associated with a single customer 122 or with a number of different customers 122.

The management component 102 may be implemented as hardware or as a combination of hardware and software. For example, the management component 102 may be a computing system of one or more computing devices configured to execute one or more software programs to perform the functions described herein. In some embodiments, the management component may include one or more of the host computing devices 104 a-104 n.

FIG. 2 illustrates a sample management component 102 in greater detail. The management component 102 can include a profile determination module 202, a placement module 204, a migration module 206, an operating metrics data store 208, and a profile data store 210. In some embodiments, the management component 102 may include more or fewer modules and data stores than those illustrated in FIG. 2. For example, there may be no separate migration module 206, when the migration feature is not implemented, or implemented by the placement module 204. In another example embodiment, there may be additional data stores for generalized customer profiles.

In operation, the profile determination module 202 can obtain operating data regarding operating metrics and resource usage by instances of a particular virtual machine instance configuration at a particular time, of all virtual machine instances associated with a particular customer 122, etc. The profile determination module 202 can analyze the operating data and develop an operating profile of the computing resources utilized by the virtual machine instance or group of virtual machine instances being profiled. For example, operating data may include historical measurements regarding the amount of memory utilized, the central processing unit (CPU) utilization, the amount of network traffic transmitted or received, the amount of hard disk space utilized, the number of disk operations, the amount of electricity utilized (e.g.: the amount utilized by the host computing device 104 that may be attributable to the virtual machine instance), the amount of network link traffic initiated, and the like. The profile determination module 202 can then determine an average for each of the measurements associated with instances of a particular virtual machine instance configuration or group of virtual machine instance configurations, and store the averages in the operating profile. The operating profile need not be limited to average measurements. For example, the operating profile may include other statistical analyses, such as the median, standard deviation, usage histogram or any other appropriate or useful data. In some embodiments, the operating profile may further be characterized according to temporal characteristics of usage, such as the time of day, day of the year, etc.

The operating profile may also be characterized according to expected measurements and operating metrics. For example, a variance from an expected performance metric, generally referred to as jitter, may be observed and included in the operating profile. Such data may be used to determine whether design goals, service-level agreements and other promises or obligations to the consumer are being met or to determine how often they fail to be met. The placement module 204 may account for jitter when making future placement decisions, endeavoring to ensure that the same operating metric will not fall outside the expected range or otherwise ensuring that consumer obligations are satisfied. In some embodiments, the operating profile may contain other data, such as latency preferences or requirements, instructions set preferences or requirements, and the like. Such data may be provided by consumers or determined through analysis of virtual machine instance operation by the profile determination module 202.

Illustratively, a service provider may provide three classes of virtual machines: small, medium, and large. Each class may be associated with a predetermined amount of each computing resource that will be reserved for use by instances of the virtual machine (e.g.: small VMs may have 2 GB RAM, medium VMs may have 8 GB RAM, large VMs may have 32 GB RAM). Customers may instantiate instances of a virtual machine configured with an operating system and application software, such as a large virtual machine configured with web server software. Measurements may be recorded regarding usage of computing resources by an instance of the large virtual machine configured with web server software. The profile determination module 202 can then calculate expected resource usage amounts for future instances of the virtual machine instance configuration when, for example, used as a web server. The expected resource usage amounts may form the basis of the operating profile determined by the profile determination module 202. The profile determination module 202 may then modify the operating profile as a data set including measurements of actual resource usage is built over time.

The profile data that is used by the profile determination module 202 may be obtained from a variety of sources. As described above, the data may be obtained from an entity associated with the virtual machine. Data may also be obtained directly from a workload analysis component of the host computing device 104 on which the virtual machine instance is executing. In some embodiments, the data can be obtained from an operating metric data store 208. The operating metric data store 208 may be integrated with the management component 102, as illustrated in FIG. 2, or it may be physically located on a separate computing device, such as a dedicated relational database management system (RDBMS) server. The operating profiles that are determined by the profile determination module 202 may be stored in a profile data store 210. Similar to the operating metric data store 208, the profile data store 210 may be integrated with the management component 102 or located on separate computing device, such as a dedicated RDBMS server.

In some network computing environments 100, there may be thousands or more of virtual machine instances to profile, and each operating profile may, for example, be based on the analysis of usage data unique to particular virtual machine instance configurations or the usage of a particular customer. In order to efficiently utilize the operating profiles to make placement decisions regarding the instantiation of virtual machine instances on host computing devices 104 a-104 n, the operating profiles may be generalized. Accordingly, a number of different virtual machine instance configurations may be associated with the same, or substantially similar, operating profiles even though there may be variances in the actual resource usage associated with each virtual machine instance configuration. For example, the profile determination module 202 may associate a virtual machine instance configuration with predefined expected usage amounts rather than storing a customized operating profile for each virtual machine instance configuration. The predefined operating profile may include utilization ranges for each computing resource that is measured. In addition, the operating profiles may be hierarchical, such that a particular virtual machine instance configuration is associated with a particular operating profile, and also with a more general operating profile that is itself associated with multiple virtual machine instance configurations.

In some embodiments, the operating profiles may be further generalized into categories. For example, a number of virtual machine instance configurations, each associated with a different amount of network usage, may be categorized as “light network applications” or “heavy network applications” depending on whether the usage measurement exceeds or falls short of some threshold. In such a categorization scheme, a virtual machine instance configuration that, when instantiated, primarily performs local computing operations and rarely utilizes a network connection may be categorized in the same “light network applications” category as a virtual machine instance configuration that often utilizes a network connection, but only for very small transmissions which may be trivial in comparison to the amount of network bandwidth available to the host computing devices 104 a-104 n on which the virtual machine instance executes. Such generalized operating profiles may also be based on a composite of two or more categories, such as “light network application/heavy CPU application” and “light network application/light CPU application.” Returning to the previous example, the two virtual machine instance configurations may be associated with different categories. The virtual machine instance configuration that, when instantiated, primarily performs local computing operations and rarely utilizes a network connection may be categorized as a “light network application/heavy CPU application,” while the virtual machine instance configuration which, when instantiated, often initiates small network transmissions may be categorized as a “light network application/light CPU application” if the CPU utilization of the virtual machine instances fall below a threshold.

As described above, the operating profiles, whether specific to a virtual machine instance configuration or generalized to a number of virtual machine instance configurations, may be used to identify a host computing device 104 a-104 n on which to place virtual machine instances. The placement module 204 may be invoked when a customer 122 initiates a computing session or when a virtual machine is otherwise instantiated. The placement module 204 may determine which operating profile is associated with the virtual machine instance at the current time. For example, the operating profile may be a customized profile including measurements of actual resource usage associated with the virtual machine instance at the current time of day, during the current month of the year, etc. In some cases, the measurements may be specific to a particular customer, such that an operating profile for a particular customer may be created and accessed. The customer-specific operating profile can apply to a specific virtual machine instance configuration or it may generally apply to multiple distinct virtual machine instance configurations. Optionally, the operating profile may be a generalized profile based on the overall character of resource usage associated with the virtual machine instance, which may also be based on the current time of day, etc. The virtual machine placement module 204 can then select a host computing device 104 on which to launch the virtual machine instance based on the resource availability of the host computing devices 104 a-104 n and the expected resource usage of the virtual machine instance determined from the operating profile.

Resource utilization may be dynamic over the lifetime of a single instance of a specific virtual machine instance configuration, and over multiple instances of the specific virtual machine instance configuration. The migration module 206 of the management component 102 may monitor the resource utilization of each executing virtual machine instance and the host computing device 104 on which the virtual machine instance is executing. When the resource utilization changes, the migration module 206, similar to the virtual machine placement module 204 described above, may select an appropriate host computing device 104 on which to place the virtual machine instance. A new instance of the virtual machine may be launched on the selected host computing device 104, and the execution state of the virtual machine instance (memory, inputs, and the like) may be copied to the new virtual machine instance. When the new virtual machine instance is ready to begin executing, the previous virtual machine instance may be terminated without a loss of data and without a substantial loss of performance. The new virtual machine instance may execute more efficiently due to the available resources.

In some embodiments, rather than instantiating a new instance of the virtual machine on a different host computing device and terminating the previous instance, resources may be reallocated. When resource utilization or performance metrics change, additional resources (e.g., memory) may be allocated to the particular virtual machine. For example, a resource may be reallocated from other virtual machines that are not expected to fully utilize the resource.

Turning now to FIG. 3, an illustrative process 300 for determining an operating profile for a virtual machine instance configuration will be described. The process 300 may be executed by a management component 102. The management component 102 may receive a request from a customer 122 or otherwise be notified to instantiate a virtual machine. After instantiating the virtual machine instance, identifying an instantiated virtual machine instance or causing the virtual machine to be instantiated, the management component 102 may monitor or otherwise receive operating data regarding computing resource utilization associated with the virtual machine instance. Based on the resource usage and operating metric data, the management component 102 can determine or update an operating profile for the virtual machine instance configuration, or update an existing operating profile. Advantageously, the operating profile may be compared with other operating profiles and generalized and the virtual machine instance configuration may be associated with a category of resource usage.

The process 300 begins at block 302. The process 300 may begin automatically, such as in response to the receipt of a request to instantiate a virtual machine. For example, the process 300 may be embodied in a set of executable program instructions and stored on a computer-readable medium drive of the computing system with which the management component 102 is associated. When the process 300 is initiated, the executable program instructions can be loaded into memory, such as RAM, and executed by one or more processors of the computing system. In some embodiments, the computing system may include multiple computing devices, such as servers, and the process 300 may be executed by multiple servers, serially or in parallel.

At block 304, the management component 102 or some other component launches a virtual machine instance. As described in detail below with respect to FIG. 5, the management component may select a host computing device 104 on which to launch the virtual machine instance based on the resources expected to be consumed by the virtual machine instance and the resources that the host computing devices 104 a-104 n currently have available. The resources expected to be consumed by the virtual machine instance or to be made available to the virtual machine instance may be determined from a preexisting operating profile, from information received from the customer 122 or other entity requesting that the virtual machine instance be launched, etc.

The process 300 may proceed to block 306 for the newly launched virtual machine instance in order to obtain operating metrics and to create or modify an operating profile. While the process 300 proceeds, any number of additional virtual machine instances may be launched and/or placed at block 304 based on the same operating profile, either as it originally existed, or as modified during the execution of the process 300 for previously launched virtual machine instances. In this way, the process 300 may be performed in any number of concurrent instances, generally corresponding to the number of virtual machine instances associated with the operating profile (or, in a hierarchy of profiles, a profile from a higher level in the hierarchy) that may be executing at a particular time.

At block 306, the resources utilized by the virtual machine instance may be monitored, and resource usage measurements and other operating metrics may be obtained. At block 308, the operating metrics may be recorded. The monitoring may be performed by the management component 102, or by some other component, such as a workload analysis component 421 of the host computing device 104 on which the virtual machine instance is executing. The operating metrics may be stored at the operating metrics data store 208. In embodiments using a workload analysis component 421, the workload analysis component 421 may store operating metrics temporarily or long-term. The workload analysis component may transmit data regarding the operating metrics to the management component 102 for storage in substantially real time, at scheduled intervals, upon virtual machine termination, at some other time, or not at all.

FIG. 4 illustrates measurement of the utilization of several resources provided to multiple virtual machine instances by a host computing device 104. As illustrated in FIG. 4, a host computing device 104 may provide computing resources, such as memory 402, a CPU 404, and a network bandwidth 406. In some embodiments, additional or fewer computing resources may be provided to virtual machine instances. For example, a virtual machine instance may not be permitted to communicate with other devices, and therefore utilization of the network interface 406 need not be measured. In another example, a host computing device 104 may provide and track utilization of hard disk space, hard disk operations, electrical power, and the like.

In some embodiments, a provider of computing resources, such as an operator of a network computing environment 100, may provide customers with a set amount of computing resources on which to execute a virtual machine instance. For example, a customer 122 may reserve for one of its virtual machine instance configurations a predetermined amount of memory, such as random access memory (RAM), a predetermined amount of computing capacity, such as CPU cores, and a predetermined amount of network bandwidth, as provided by a network interface. Memory 402 of a host computing device 104 may be segregated into portions 410, 412, 414 which are reserved for single virtual machine instances (e.g.: portions 412, 414) or for the operation of the host computing device 104 and other internal procedures (e.g.: portion 410). The portion reserved for operation of the host computing device 104 may include a hypervisor for assisting in the launch, execution, and termination of virtual machine instances, an operating system, drivers, and the like. In addition, the host computing device 104 may include a workload analysis component 421 which monitors resource utilization and optionally communicates with the management component 102. The workload analysis component 421 may also reside in the memory space 410, and may be integrated into the hypervisor 420 or may be an independent component which shares the memory space 410. In some embodiments, the workload analysis component 421 may reside in a memory space 412, 414 reserved for customer virtual machine instances. In such cases, the workload analysis component 421 may be integrated into the virtual machine instance configurations or included in the virtual machine instance upon instantiation. In further embodiments, the workload analysis component 421 may reside in a separate memory space reserved for it, or may be implemented as a component, such an independent hardware device, which does not share the memory 402 of the host computing device 104.

In many cases, a virtual machine instance may not utilize the entire portion of a resource that is reserved for it. For example, VM1 422, illustrated in FIG. 4, may be a virtual machine instance of a customer 122, and may be launched into memory space 412, the entirety of which is reserved for use by VM1 422. In operation, VM1 422 may not utilize the entire memory space 412, and in some cases may utilize on a small fraction of the reserved memory space 412. At times, however, the utilization of the memory space 412 may change, and VM1 422 may utilize substantially all of the memory space 412. The workload analysis component 421 may monitor these changes and record measurements and other data, such as the time of day, the specific virtual machine instance configuration, or which other virtual machine instances, if any, were executing on the host computing device 104. The workload analysis component 421 may transmit the data to the management component 102 or to a data store. In some embodiments, the workload analysis component 421 may temporarily store the data and later transfer it to the management component 102, such as on a schedule, or in response to a triggering event, such as the termination of VM1 422. Similar to measuring and recording data about the utilization of memory 402, the workload analysis component 421 or some other component may monitor usage of the CPU 404, network interface 406, or any other computing resource utilized by VM1 422.

Data may be obtained and recorded regarding any variances from expected or preferred operating metrics. For example, resource usage measurements and other operating metrics may be recorded and compared to the operating profile in order to determine whether there is a variance from an expected or preferred metric. In some cases, the operating metrics may be recorded on a customer-by-customer basis. Data regarding a variance may be recorded so that future placement or migration decisions may be made based on the variance. In addition, data regarding off-host resources, such as latency, link traffic, and the like may be recorded. The workload analysis component 421 may record such data, or some component external to the host (e.g., the management component 102 or a switch) may observe the operating metrics. In some embodiments, resource usage that does not necessarily reduce the availability of the resource may be determined. For example, if a virtual machine instance or application software running thereon performs certain cryptographic operations or is observed calling certain cryptographic functions or instructions, such data may be recorded. The placement module 204 or migration module 206 may consider such data when launching or migrating an instance of the virtual machine. A host computing device may be selected which provides more efficient or more powerful cryptographic instructions, such a device supporting Intel® Advanced Encryption Standard (AES) New Instructions (AES-NI) or similar device.

At block 310, the profile determination module 202 or some other module of the management component 102 may modify an operating profile associated with the virtual machine instance, or create a new operating profile. As described above, operating profiles may include information about typical or expected resource usage, variances from expected or desired operating metrics, and the like. For example, the operating profile may consist of average measurements for each of a number of different instances of a single virtual machine instance configuration. Each resource may be associated with multiple measurements which correspond to operating based on a particular customer, a time of day, a day of the year, or other environmental factors.

In some embodiments, each resource of the operating profile may be associated with a score or some other indication of utilization rather than a statistical measurement. For example, each resource may be assigned a score of 1-10, where higher numbers are associated with the heaviest and/or most frequent users of a resource. In some embodiments, the operating profiles may be generalized further. A predefined set of generalized operating profiles may cover ranges of measurements or scores for each resource. For example, the virtual machine instance configuration from which VM1 422 was instantiated may be assigned to one generalized operating profile if, during nighttime hours, VM1 422 utilizes no more than 25% of its memory space 412 but utilizes almost 100% of its CPU availability. The generalized operating profiles may include multiple ranges of measurements for each resource, depending on the time of day or other factors. Returning the previous example, the virtual machine instance configuration from which VM1 422 is instantiated may be instead assigned to a different predefined operating profile if the virtual machine instances typically utilize resources in that manner described above during nighttime hours, but during daytime hours it utilizes 50% of both its memory segment 412 and CPU availability. Multiple generalized operating profiles may be assigned to particular virtual machine instance configurations based on usage by particular customers. For example, each customer that users the virtual machine instance configuration may be associated with a different operating profile.

In some embodiments, each customer 122 may be associated with a generalized operating profile even though it has a number of different virtual machine instance configurations, and even though each virtual machine instance configuration may utilize resources differently. A customer 122 may have one virtual machine instance configuration, such as the one from which VM1 422 in FIG. 4 is instantiated, which may be independently profiled as a light CPU application, while another virtual machine instance configuration, such as the one from which VM2 424 is instantiated, may be independently profiled as a heavy CPU application. The customer 122 may be profiled as a moderate CPU user, because its average CPU use is moderate. Optionally, the customer 122 may be profiled as a heavy CPU user, because it has at least one virtual machine instance configuration which is a heavy CPU application. In other embodiments, customers may have several associated operating profiles for each virtual machine image configuration. Different customers or users of substantially the same virtual machine image, such as VM1 422, may use different amounts of resources, even though the virtual machine image is a common configuration. A given customer starting a particular VM may be more likely to use that VM in the same way as previously recorded, and consume approximately the same resources.

Operating profiles for each virtual machine instance configuration may be stored in the profiles data store 210. The actual measurements for each profile may be stored in the operating profile, or an ID or other indication of which category or generalized operating profile the virtual machine instance configuration is associated with may be stored. In embodiments which determine and utilize customer profiles instead of or in addition to virtual machine profiles, customer profile data may be stored in the same data store 210 or in a different data store.

At block 312, related or generalized operating profiles may be created or modified. For example, higher-level profiles may be created or modified if hierarchical profiles are used. Historical operating metrics may be accessed from the operating metrics data store 208 for each virtual machine instance configuration associated with the high-level operating profile, in some cases regardless of which lower-level profiles the virtual machine instance configurations are associated with. Statistical analyses may be performed and operating metric variances may be determined as described above. Advantageously, the modified high-level or generalized operating profile may be accessed and used by the placement module 204 or migration module 206 when instantiating or migrating virtual machine instances that are different from the virtual machine instance associated with the current execution of the process 300. Accordingly, the recorded operating metrics associated with one virtual machine instance may be used to fine tune the placement and execution of other virtual machine instances, even those which are not instantiated from the same virtual machine instance configuration.

Turning now to FIG. 5, an illustrative process 500 for determining placement of virtual machine instances based on operating profiles will be described. The process 500 may be executed by a management component 102. The management component 102 may receive a request from a customer 122 or may otherwise be notified to launch an instance of a virtual machine from a specific virtual machine instance configuration or image. The management component 102 can identify host computing devices 104 a-104 n which are able to host the virtual machine instance and determine the current status of the host computing devices 104 a-104 n with respect to available computing resources. Advantageously, the management component 102 may also obtain an operating profile for the virtual machine instance configuration to be instantiated, and determine which of the available host computing devices 104 a-104 n may most efficiently host the virtual machine from the standpoint of available resources. A host computing device 104 may be selected which is already executing virtual machines and which has committed most or all of its resources to host virtual machines already executing. Based on operating profiles of the currently executing virtual machine instances and of the virtual machine instance to be launched, the management component 102 may launch the virtual machine instance on the host computing device 104 if the management component 102 determines that the host computing device 104 can provide the computing resources that the virtual machine instances will likely consume. In some cases, this may include oversubscribing resources (e.g., allocating the same resources to multiple virtual machine instances). Moreover, the management component 102 can monitor the execution of the virtual machine instances on the host computing device 104, and transfer execution of one or more virtual machine instances to another host computing device 104 if there are not enough computing resources to satisfy each virtual machine instance.

The process 500 begins at block 502. The process 500 may be initiated automatically, such as in response to the receipt of a request to launch a virtual machine instance. For example, the process 500 may be embodied in a set of executable program instructions and stored on a non-transitory computer-readable medium drive of the computing system with which the management component 102 is associated. When the process 500 is initiated, the executable program instructions can be loaded into memory, such as RAM, and executed by one or more processors of the computing system. In some embodiments, the computing system may include multiple computing devices, such as servers, and the process 500 may be executed by multiple servers, serially or in parallel.

At block 504, the management component 102 may receive a request or some other notification to initialize a virtual machine instance. The notification may be received from a customer 122, a host computing device 104, or some other component or entity. In some embodiments, a virtual machine instance may request initialization of another virtual machine instance, another instance of the same virtual machine instance configuration or image, etc.

At block 506, the VM placement module 204 or some other module of the management component 102 may obtain an operating profile for the virtual machine instance to be launched. The operating profile may be loaded from the profile data store 210 or obtained from some other source. The VM placement module 204 may inspect the operating profile to determine which resources the virtual machine instance is likely to utilize and in which quantity. As described above, the operating profile of the virtual machine instance configuration may be different depending on environmental factors, such as the time of day. In such cases, the VM placement module 204 of the management component 102 can consider such environmental factors when inspecting the operating profile.

At block 508, the VM placement module 204 or some other module of the management component 102 may select a host computing device 104 on which to launch the virtual machine instance based on available resources and the operating profile. For example, a network computing environment 100 may include a number of host computing devices 104 a-104 n. The host computing devices 104 a-104 n need not be identical; some may have more or less RAM than others, more or less powerful processors or a different number of processors, etc. The VM placement module 204 may select a host computing device 104 on which to launch the virtual machine instance based on the expected resource utilization as identified by the operating profile and by the resources that each computing device makes available.

In some embodiments, a host computing device 104 may be configured to host a set number of instances of a particular virtual machine or class of virtual machines. As shown in FIG. 4, the host computing device 104 may have an amount of memory 402 such that it can reserve a predetermined memory space 410 for the hypervisor 420, and two additional memory spaces 412, 414 of a predetermined size for virtual machines. Two virtual machine instances 422, 424 may be launched on the host computing device 104, with each virtual machine instance 422, 424 assigned a separate memory space 412, 414. A customer 122 may reserve a particular amount of a resource to be available to its virtual machine instances 422, 424, such as by selecting a particular class of virtual machine (e.g.: small, medium or large as described above) to configure. The memory spaces 412, 414 may correspond to the maximum allowable amount of resources available to the virtual machine instances 422, 424, as reserved by the customer 122. However, in practice the virtual machine instances 422, 424 may not utilize the entire amount of a computing resource that is reserved for them. For example, as seen in FIG. 4, the virtual machine instances 422, 424 are only utilizing a fraction of the memory spaces 412, 414 that are reserved for them. It may be advantageous to utilize such excess memory space and other excess computing resources so as to reduce the number of host computing devices 104 a-104 n required to service all currently executing virtual machine instances or to more efficiently utilize the resources of those host computing devices 104 a-104 n which are operating.

FIG. 6 illustrates a host computing device 104 with oversubscribed computing resources. A third virtual machine instance 426 has been launched on the host computing device 104 even though the host computing device 104 only contains two memory spaces 412, 414 available for virtual machine instances. Based on the operating profile associated with each of the virtual machine instances 422, 424, 426, the VM placement module 204 may determine that VM2 422 utilizes only a fraction of its available memory space 414, and VM3 426 also uses only a fraction of its available memory space when it is launched. Therefore, the VM placement module 204 may launch VM3 426 on the same computing device as VM2 424 and assign them to the same memory space 414.

As seen in FIG. 6, VM1 422 utilizes substantially all of its memory space 412, and therefore the VM placement module 204 may not assign another virtual machine instance to the same memory space 412 due to the operating profile of VM1 422. However, VM1 422 utilizes only a small amount of CPU capacity 442, and therefore a host computing device 104 on which VM1 422 is executing may be a candidate for oversubscription if the operating profiles of the virtual machine instances are complementary. In the example illustrated in FIG. 6, VM2 424 utilizes a large amount of CPU capacity 444. However, if the operating profile of VM3 426 indicates that it is a light user of CPU capacity, then the three virtual machine instances VM1 422, VM2 424, and VM3 426 may be candidates for oversubscription due to the complementary, rather than overlapping, operating profiles of the virtual machine instance configurations from which they are instantiated. Additional resources may be factored into an oversubscription determination in order to ensure that each virtual machine instance executing on a host computing device 104 has readily available to it the amount of each computing resource that it typically requires. For example, network bandwidth utilization 406, as illustrated in FIG. 6, also supports the oversubscription determination example described above because, even though the operating profile for VM3 426 may indicate that it is a heavy network bandwidth application, VM1 422 and VM2 424 utilize only a small amount of network bandwidth.

At block 510, the VM placement module 204 or some other module of the management component 102 may place the virtual machine instance on the host computing device 104 that is identified in block 508. As described above, the virtual machine instance may be placed on a host computing device 104 with other virtual machine instances which have reserved amounts of computing resources totaling or exceeding the amount provided by the host computing device 104. In some cases, the oversubscription may be substantial.

FIG. 7 illustrates a substantially oversubscribed host computing device 104. As described above, customers 122 may reserve a specified amount of computing resources for use by virtual machine instances of the customer. However, in some cases a customer 122 may have substantially overestimated the amount of computing resources that its virtual machine instances may actually use. In extreme cases, customer 122 may reserve a large quantity of computing resources, launch virtual machine instances associated with those resources, and then let the virtual machine instances sit idle or otherwise substantially underuse the reserved resources. Over the course of time, operating profiles may be developed for the virtual machine instances or for the customer 122 which reflect the substantial underuse of reserved resources. The management component 102 may then launch a large number of such low-utilization virtual machine instances on a single host computing device 104. The host computing device 104 of FIG. 7 includes seven different virtual machine instances 422, 424, 426. 722, 724, 726, 728 sharing computing resources which may typically be reserved for only two virtual machine instances, as described above with respect to FIGS. 4 and 6. However, there is still excess capacity of each of the three computing resources 402, 404, 406. In some cases, hundreds or more of virtual machine instances may be placed on a host computing device 104, such as when the host computing device 104 has a large amount of available computing resources and the virtual machine instances are substantially idle.

At block 512, the resource utilization of each virtual machine instance may be monitored. Over the lifetime of the specific virtual machine instances, the workload analysis component 421 may monitor operating and notify the management component 102 if one of the virtual machine instances begins to utilize resources at a level that is not serviceable by an oversubscribed host computing device 104, or if the resource usage or an operating metric otherwise differs from an expected or desired amount. In some embodiments, the management component 102 may perform the monitoring.

At block 514, the management component 102 can determine whether resource usage or an operating metric differs from an expected or desired amount. For example, the management component can determine whether a change in resource usage exceeds a threshold or may otherwise cause undesirable performance degradation. A virtual machine instance which begins to utilize more of a computing resource than expected, based on its operating profile and the placement determined by the management component 102, may be transferred to a host computing device 104 that is oversubscribed to a lesser extent, or to a host computing device 104 that is not oversubscribed at all. In such cases, execution of the process 500 can return to block 508, where the VM migration module 206 or some other management component 102 determines to which computing device to transfer the virtual machine 842.

FIG. 8 illustrates a host computing device 104 a which has oversubscribed CPU capacity 404 a. A virtual machine instance VM4 842 may begin to consume a large amount of available CPU capacity 404 a, in contradiction to its operating profile. However, the customer 122 associated with the virtual machine instance VM4 842 may have reserved a large amount of CPU capacity for the virtual machine instance configuration from which VM4 842 is instantiated, and therefore it may be desirable to provide the virtual machine instance VM4 842 with more CPU capacity than an oversubscribed host computing device 104 a can provide. As shown in FIG. 8, the host computing device 104 b may be a candidate for such a transfer. The virtual machine instance VM8 844 is currently consuming only a small fraction of the CPU capacity 404 b available on the host computing device 104 b, and the management component 102 may determine that the operating profile associated with VM8 844 indicates that it is not likely to consume more. The VM migration module 206 can initiate transfer of VM4 842 from host computing device 104 a to host computing device 104 b.

Transfer of a virtual machine instance may include first launching an instance of the same virtual machine instance configuration or image on the target host computing device 104 b while the virtual machine instance on the source host computing device 104 a continues to execute. The execution state of the virtual machine instance on the source host computing device 104 b, including the data in the memory space or hard disk associated with the virtual machine instance, network connections established by the virtual machine instance, and the like, can then be duplicated at the target host computing device 104 b. The virtual machine instance on the source host computing device 104 a can be terminated, and the virtual machine instance on the target host computing device 104 b can continue execution from that point.

In some embodiments, the initial placement or transfer of a software workload (e.g., an application or storage node) may be associated with virtual machine instance use of, or interaction with, the software workload, even though the software workload may not necessarily be a virtual machine instance itself. For example, a software workload may consist of a storage node implemented as an agent storing data according to the expectations of virtual machine instances that may be using the data. In such cases, initial placement or transfer of the workload (in this case, the storage node) may be based on placing it near or otherwise making it more accessible by the virtual machine instance or instances that will be using it.

Depending on the embodiment, certain acts, events, or functions of any of the processes or algorithms described herein can be performed in a different sequence, can be added, merged, or left out altogether (e.g., not all described operations or events are necessary for the practice of the algorithm). Moreover, in certain embodiments, operations or events can be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors or processor cores or on other parallel architectures, rather than sequentially.

The various illustrative logical blocks, modules, routines, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. The described functionality can be implemented in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosure.

The steps of a method, process, routine, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of a non-transitory computer-readable storage medium. An exemplary storage medium can be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

Conditional language used herein, such as, among others, “can,” “could,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. The terms “comprising,” “including,” “having,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list.

Conjunctive language such as the phrase “at least one of X, Y and Z,” unless specifically stated otherwise, is to be understood with the context as used in general to convey that an item, term, etc. may be either X, Y, or Z, or a combination thereof. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of X, at least one of Y and at least one of Z to each be present.

While the above detailed description has shown, described, and pointed out novel features as applied to various embodiments, it can be understood that various omissions, substitutions, and changes in the form and details of the devices or algorithms illustrated can be made without departing from the spirit of the disclosure. As can be recognized, certain embodiments of the inventions described herein can be embodied within a form that does not provide all of the features and benefits set forth herein, as some features can be used or practiced separately from others. The scope of certain inventions disclosed herein is indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope. 

What is claimed is:
 1. A system for managing shared computing resources, the system comprising: one or more processors; and a computer-readable memory storing executable instructions that, when executed by the one or more processors, configure the system to: receive a request to initialize a first instance of a first virtual machine, the first instance associated with a first expected usage amount of a computing resource and a first reserved amount of the computing resource, wherein the first expected usage amount is based at least in part on operating metrics determined from previously running at least an instance of a similar virtual machine, and wherein the first reserved amount corresponds to an amount of the computing resource reserved for the first instance by a customer associated with the first instance; select a computing device of a plurality of computing devices configured to provide the computing resource, wherein a second instance of a second virtual machine is executing on the computing device, wherein the second instance is associated with a second expected usage amount and a second reserved amount of the computing resource, and wherein the selected computing device is selected based at least partly on: calculating a total reserved amount, the total reserved amount comprising a sum of the first reserved amount and the second reserved amount; determining that the total reserved amount is greater than an amount of the computing resource available on the computing device; in response to the determination that the total reserved amount is greater than the amount of the computing resource available on the computing device, calculating a total expected usage amount, the total expected usage amount comprising a sum of the first expected usage amount and second expected usage amount; and determining that the total expected usage amount is less than the amount of the computing resource available on the computing device; and cause, at least in part, the first instance to be initialized on the selected computing device, wherein the first instance and the second instance share the available amount of the computing resource.
 2. The system of claim 1, wherein the computing resource comprises one of central processing unit (CPU) capacity, memory space, network bandwidth, hard disk space, or electrical power.
 3. The system of claim 1, wherein the first instance and the second instance are associated with different customers.
 4. The system of claim 1, wherein the executable instructions further configure the system to: cause, at least in part, a third instance of a third virtual machine to be initialized on the selected computing device, wherein the third instance is associated with a third expected usage amount and a third reserved amount of the computing resource, wherein the amount of the computing resource available on the computing device is further greater than a sum of the first expected usage amount, the second expected usage amount, and the third expected usage amount, and wherein the first instance, the second instance, and the third instance share the available amount of the computing resource.
 5. The system of claim 1, wherein the executable instructions further configure the system to: obtain a first measurement of usage of the computing resource by the first instance and a second measurement of usage of the computing resource by the second instance; and in response to determining that the first measurement or the second measurement exceeds a threshold, transfer the first instance or the second instance to a second computing device.
 6. A computer-implemented method for managing shared computing resources, the computer-implemented method comprising: receiving, by a data center management system comprising one or more computing devices, a request to initialize a first software workload associated with a first operating constraint and a first reserved resource amount, wherein the first operating constraint is based at least in part on a first plurality of historical operating metrics regarding operation of the first software workload on a host computing device, and wherein the first reserved resource amount comprises an amount of a computing resource reserved for use by the first software workload; in response to the request, identifying a first host computing device of a plurality of host computing devices based at least on the first operating constraint and one or more operating characteristics associated with the first host computing device, wherein the first host computing device comprises a second software workload associated with a second operating constraint and a second reserved resource amount, and wherein the identifying comprises: calculating a total reserved resource amount, the total reserved resource amount comprising a sum of the first reserved resource amount and the second reserved resource amount; determining that an available amount of the computing resource is less than the total reserved resource amount; and in response to the determination that the available amount of the computing resource is less than the total reserved resource amount, determining that the one or more operating characteristics of the first host computing device are sufficient to satisfy both the first operating constraint and the second operating constraint during operation of the first software workload and the second software workload; and causing, at least in part, the first software workload to be placed on the first host computing device.
 7. The computer-implemented method of claim 6, wherein the first software workload or the second software workload comprises a virtual machine instance, an application instance, or an operating system.
 8. The computer-implemented method of claim 6, wherein the first software workload comprises a storage node, and wherein the identifying further comprises determining that placing the storage node on the first host computing device satisfies an operating constraint associated with a virtual machine that will use the storage node.
 9. The computer-implemented method of claim 6, wherein at least one of the one or more operating characteristics comprises memory capacity, central processing unit (CPU) capacity, network bandwidth, network latency, position within a network topology, instruction set, variance of a performance metric, or electrical power capacity.
 10. The computer-implemented method of claim 6, wherein the first operating constraint or the second operating constraint relate to memory utilization, central processing unit (CPU) utilization, network bandwidth utilization, network latency, position within a network topology, instruction set availability, variance of a performance metric, or power utilization.
 11. The computer-implemented method of claim 6, wherein the first operating constraint relates to a first expected usage amount of the computing resource, wherein the second operating constraint relates to a second expected usage amount of the computing resource, and wherein the first software workload and the second software workload are enabled to concurrently consume the first expected usage amount and the second expected usage amount, respectively, of the computing resource.
 12. The computer-implemented method of claim 11, wherein an operating characteristic of the one or more operating characteristics comprises the available amount of the computing resource, the computer-implemented method further comprising: preventing, at least in part, the first software workload and the second software workload from aggregately consuming more than the available amount of the computing resource.
 13. The computer-implemented method of claim 6 further comprising: causing, at least in part, a third software workload to be initialized on the first host computing device, wherein the third software workload is associated with a third reserved resource amount and a third operating constraint; wherein the method further comprises calculating an updated total reserved resource amount, the updated total reserved resource corresponding to a sum of the first reserved resource amount, the second reserved resource amount, and the third reserved resource amount; and wherein the identifying further comprises: determining that the available amount of the computing resource is less than the updated total reserved resource amount; and in response to the determination that the available amount of the computing resource is less than the updated total reserved resource amount, determining that the one or more operating characteristics of the first host computing device are sufficient to satisfy each of the first operating constraint, the second operating constraint, and the third operating constraint during operation of the first software workload, the second software workload, and the third software workload.
 14. The computer-implemented method of claim 6 further comprising: obtaining one or more operating metrics regarding operation of the first software workload on the first host computing device; determining, based at least in part on the one or more operating metrics, that the one or more operating characteristics of the first host computing device are no longer sufficient to satisfy the first operating constraint; and in response to the determination, transferring the first software workload or the second software workload to a second host computing device.
 15. The computer-implemented method of claim 6 further comprising: obtaining one or more operating metrics regarding operation of the second software workload on the first host computing device; determining, based at least in part on the one or more operating metrics, that the one or more operating characteristics of the first host computing device are no longer sufficient to satisfy the second operating constraint; and in response to the determination, transferring the first software workload or the second software workload to a second host computing device.
 16. The computer-implemented method of claim 6, further comprising: obtaining one or more operating metrics regarding the one or more operating characteristics of the first host computing device; determining, based at least in part on the one or more operating metrics, that the one or more operating characteristics of the first host computing device are no longer sufficient to satisfy the first operating constraint or the second operating constraint; and in response to the determination, transferring the first software workload or the second software workload to a second host computing device.
 17. A system for managing shared computing resources, the system comprising: one or more processors; a computer-readable memory storing executable instructions that, when executed by the one or more processors, configure the system to: receive a request to initialize a first computer-executable program associated with a first operating constraint and a first reserved resource amount, wherein the first operating constraint is based at least in part on a first plurality of historical operating metrics regarding operation of the first computer-executable program on a host computing device, and wherein the first reserved resource amount comprises an amount of a computing resource reserved for use by the first computer-executable program; and identify a first host computing device of a plurality of host computing devices, the first host computing device associated with one or more operating characteristics, wherein the first host computing device comprises a second computer-executable program associated with a second operating constraint and a second reserved resource amount, and wherein the first host computing device is identified based at least in part on: calculating a total reserved resource amount, the total reserved resource amount comprising the sum of the first reserved resource amount and the second reserved resource amount; determining that an available amount of the computing resource is less than the total reserved resource amount; and in response to the determination that the available amount of the computing resource is less than the total reserved resource amount, determining that the one or more operating characteristics satisfy both the first operating constraint and the second operating constraint; and cause, at least in part, the first computer-executable program to execute on the first host computing device.
 18. The system of claim 17, wherein the first computer-executable program or the second computer-executable program comprises one of a virtual machine instance, an application instance, or an operating system.
 19. The system of claim 17, wherein at least one of the one or more operating characteristics comprises memory capacity, central processing unit (CPU) capacity, network bandwidth, network latency, position within a network topology, instruction set, variance of a performance metric, or electrical power capacity.
 20. The system of claim 17, wherein the first operating constraint or the second operating constraint relate to memory utilization, central processing unit (CPU) utilization, network bandwidth utilization, network latency, position within a network topology, instruction set availability, variance of a performance metric, or power utilization.
 21. The system of claim 17, wherein the first operating constraint relates to a first expected usage amount of the computing resource, wherein the second operating constraint relates to a second expected usage amount of the computing resource, and wherein the first computer-executable program and the second computer-executable program are enabled to concurrently consume the first expected usage amount and the second expected usage amount, respectively, of the computing resource.
 22. The system of claim 21, wherein the executable instructions further configure the system to prevent, at least in part, the first computer-executable program and the second computer-executable program from aggregately consuming more than the available amount of the computing resource.
 23. The system of claim 17, wherein the executable instructions further configure the system to: receive a request to initialize a third computer-executable program associated with a third operating constraint and a third reserved resource amount, wherein the third operating constraint is based at least in part on a second plurality of historical operating metrics regarding operation of the third computer-executable program on a host computing device, and wherein the third reserved resource amount comprises an amount of a third computing resource reserved for use by the third computer-executable program; cause, at least in part, the third computer-executable program to be initialized on the first host computing device; and calculate an updated total reserved resource amount, the updated total reserved resource amount corresponding to a sum of the first reserved resource amount, the second reserved resource amount, and the third reserved resource amount; wherein the identifying further comprises: determining that the available amount of the computing resource is less than the updated total reserved resource amount; and in response to the determination that the available amount of the computing resource is less than the updated total reserved resource amount, determining that the one or more operating characteristics of the first host computing device are sufficient to satisfy each of the first operating constraint, the second operating constraint, and a third operating constraint during operation of the first computer-executable program, the second computer-executable program, and the third computer-executable program.
 24. The system of claim 17, wherein the executable instructions further configure the system to: obtain one or more operating metrics regarding operation of the first computer-executable program on the first host computing device; determine, based at least in part on the one or more operating metrics, that the one or more operating characteristics of the first host computing device are no longer sufficient to satisfy the first constraint; and in response to the determination, transfer the first computer-executable program or the second computer-executable program to a second host computing device.
 25. The system of claim 17, wherein the executable instructions further configure the system to: obtain one or more operating metrics regarding operation of the second computer-executable program on the first host computing device; determine, based at least in part on the one or more operating metrics, that the one or more operating characteristics of the first host computing device are no longer sufficient to satisfy the second constraint; and in response to the determination, transfer the first computer-executable program or the second computer-executable program to a second host computing device.
 26. The system of claim 17, wherein the executable instructions further configure the system to: obtain one or more operating metrics regarding the one or more operating characteristics of the first host computing device; determine, based at least in part on the one or more operating metrics, that the one or more operating characteristics of the first host computing device are no longer sufficient to satisfy the first constraint or the second constraint; and in response to the determination, transfer the first computer-executable program or the second computer-executable program to a second host computing device. 